1. Field of the Invention
The present invention relates generally to an adaptive equalizer, and more specifically to an adaptive equalizer using a recursive least-squares (RLS) algorithm into which a forgetting factor is introduced. The adaptive equalizer is well suited for equalizing waveform distortion caused by multipath fading in a digital radio communications system. The forgetting factor is a memory factor for exponentially weighted RLS algorithms as is known in the art.
2. Description of the Prior Art
Digital radio transmission is susceptible to multipath fading or the like and invites waveform distortion of signal quality. In order to minimize this problem, the recursive least-squares (RLS) algorithm has gained considerable popularity. The RLS algorithm has been discussed in a book entitled "Adaptive Filters" edited by C. F. N. Cowan and P. M. Grant and published by Prentice-Hall, Inc., Englewood Cliffs, N.J., 1985, pages 29-35, by way of example.
However, the adaptive filter using the RLS algorithm has been found insufficient in rapidly tracking time varying incoming signals. In order to cope with this difficulty, an RLS algorithm using a forgetting factor has been proposed in an article entitled "Theoretical Analysis on RLS Adaptive Equalizer Performance in Mobile Radio Transmission" written by Hiroshi Suzuki, et al. in Technical Report published by Electronics Information & Communications Association (Japan), RSC 89-46, 1989, pages 25-30, or in an article entitled "Performance of a Decision Feedback Equalizer under Frequency Selective Fading in Land Mobile Communications" written by Makito MAKAJIMA, et al. published by the same Japanese Association as mentioned above, Paper B-II, Vol. J72-B-II, No. 10, October, 1989, pages 515-523.
The forgetting factor used in each of the above-mentioned technical papers, has been fixed to a constant value between 0 and 1. As the forgetting factor approaches zero, the fast tracking or converging of quickly time varying incoming signals can be achieved but noise cancellation is lowered. Contrarily, as the forgetting factor approaches 1, noises are effectively cancelled but the tracking of quickly time varying signals speed is lowered. Under the transmission path conditions where a signal-to-noise ratio is high, it is preferred that a forgetting factor is set to a low value for achieving high tracking or convergence of the time varying signals.
However, according to the known techniques, the forgetting factor is set to a fixed high value in the vicinity of 1 (0.95 for example) in order to maintain effective noise cancellation. Accordingly, the fast tracking of the time varying signals is sacrificed even in the presence of a high signal-to-noise ratio.